New findings show fixed-kernel discriminators are weaker than feature-learning ones.
problem Comparing performance of fixed-kernel and feature-learning discriminators.
method Using function classes F2 and F1, constructing pairs of distributions, and linking IPMs with sliced Wasserstein distances. result Fixed-kernel IPM and SD cannot discriminate certain distributions that feature-learning IPM and SD can.
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
problem Sources of discrimination in ML models and their impact on performance.
method Quantifying aleatoric and epistemic discrimination using statistical experiments and model accuracy.
result State-of-the-art fairness interventions are effective at removing epistemic discrimination but not aleatoric discrimination in datasets with missing values.
Proposes a sparse classifier for discriminative Gaussian Mixture Models.
problem Softmax-based discriminative models assume unimodality, leading to parameter redundancy.
method Sparse Bayesian learning for GMM-based discriminative model, reducing parameters and complexity.
result The SDGM outperforms existing softmax-based discriminative models.
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. T…
Proposes a curriculum-based dropout discriminator for domain adaptation.
problem Improving domain adaptation using deep learning networks trained on large labeled datasets.
method Introduces a curriculum-based dropout discriminator that gradually increases sample variance and uses reverse gradients to align source and target feature representations.
result The proposed model outperforms state-of-the-art results in domain adaptation tasks.
A new method removes policy optimization in adversarial imitation learning.
problem Adversarial imitation learning's delicate alternated optimization.
method Explicitly condition discriminator on two policies, solving generator's optimization problem directly.
result Simpler approach competitive to prevalent methods.
New method optimizes kernel feature maps for better classification.
problem High computational and memory complexity of standard kernel methods.
method Discriminant Information criterion for optimizing kernel feature maps.
result Improved optimization and generalization performances over state-of-the-art methods.
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-the-art DL methods have additional constraints included in the learning stages. These various constraints, however, lead to additional computa…
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for trainin…
QFDA combines machine learning and information theory for image classification.
problem Lack of literature on combining machine learning and information theory.
method Quantized Fisher Discriminant Analysis (QFDA) using a cost function for rate-distortion optimization.
result QFDA achieves at least as good classification accuracy as FDA on quantized images.
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns dis…
DNLL loss improves deep LDA accuracy and consistency.
problem Pathological solutions in unconstrained Deep LDA.
method Introducing Discriminative Negative Log-Likelihood (DNLL) loss.
result Deep LDA trained with DNLL produces clean latent spaces and better calibrated probabilities.
Discriminators can be good feature extractors despite their task focus.
problem Discriminators' features are often considered useless for downstream tasks.
method Theoretical analysis and feature space examination to understand discriminator's role.
result Discriminator features are robust and can prevent mode collapse, making them useful for transfer learning.
Machine learning algorithms can unintentionally discriminate; tools detect and fix this.
problem Unintentional discrimination in machine learning algorithms.
method Statistical tools to detect and eliminate biases.
result Tools can identify and mitigate algorithmic discrimination.
Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the performance of the generator and discriminator is critical, since a discriminator that achieves very high accuracy will produce relatively uninf…
This article guides data scientists on avoiding discrimination in machine learning.
problem Machine learning systems can create or exacerbate societal disparities.
method Provides a taxonomy of practices and measures to mitigate discrimination.
result Data scientists should be intentional about modeling and reducing discriminatory outcomes.
Dual adversarial domain adaptation improves knowledge transfer between labeled and unlabeled domains.
problem Transfer knowledge from labeled to unlabeled domains using unsupervised methods.
method Adopt a discriminator with 2K-dimensional output for both domain-level and class-level alignments. Design a dual adversarial mechanism to pit two discriminators against each other.
result Our method outperforms state-of-the-art domain adaptation methods on real-world datasets.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
GAN-based semi-supervised learning improves classifier generalization.
problem Improving classifier performance with limited labeled data.
method Theoretical analysis of GAN-SSL, proving equivalence of discriminator optimization and supervised learning, and exploring conditions for perfect discriminator.
result GAN-SSL theoretically outputs a perfect discriminator on both labeled and unlabeled data.
Fairness in Naive Bayes classifiers by identifying and eliminating discrimination patterns.
problem Ensuring fairness in machine learning models that use partial observations.
method Discover and eliminate discrimination patterns in naive Bayes classifiers through iterative learning.
result An algorithm that learns fair naive Bayes classifiers by removing discrimination patterns.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee f…
The paper proposes using a discriminator for both domain adaptation and pseudo labeling confidence.
problem Improving generalization of classifiers trained on labeled source data to unlabeled target data.
method Multi-purposing the discriminator to learn domain-invariant feature representations and generate pseudo labels based on confidence.
result The approach enhances classifier performance by providing confidence measures for pseudo labels.
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label set…
The study uses a multi-armed bandit model to analyze and mitigate hiring discrimination.
problem Hiring discrimination due to insufficient data on worker skill and characteristics.
method Multi-armed bandit model to simulate firms' learning process and policy solutions.
result Temporary affirmative actions effectively alleviate discrimination caused by data insufficiency.
Proposes TFDF to learn transferable and discriminative features for unsupervised domain adaptation.
problem Difficult to induce supervised classifier without labeled data in unsupervised domain adaptation.
method TFDF optimizes transferability and discriminability by aligning distributions and minimizing class confusion.
result TFDF achieves better performance on real-world datasets compared to existing methods.
Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domai…
End-to-end CCA optimizes both discriminative and latent space projections for multi-view learning.
problem Lack of class label information in CCA for multi-view learning tasks.
method Simultaneously optimizes a CCA-based and a task objective in an end-to-end manner to learn a non-linear CCA projection.
result Significant improvement in cross-view classification, regularization with a second view, and semi-supervised learning.
Proposes KMvDA for object recognition from multi-view data.
problem Recognizing objects from different views, even when views are heterogeneous.
method Introduces kernel multi-view discriminant analysis (KMvDA) and uses random Fourier features (RFF) for large-scale learning.
result KMvDA and RFF approximation improve object recognition from multi-view data.
Proposes improved classification via transfer learning with regularized linear discriminant analysis.
problem High dimensionality and small sample sizes lead to poor classification performance.
method Regularized random-effects linear discriminant analysis, combining ridge estimates from target and source models.
result Explicit derivation of asymptotic weights and classification error rates in high-dimensional settings.
Maximizes coding rate difference for robust, discriminative features.
problem Learning robust, discriminative features from high-dimensional data.
method Maximal Coding Rate Reduction (MCR^2) principle.
result Significantly more robust to label corruptions in classification.
Discriminative clustering uses mutual information to cluster data.
problem Clustering data into cohesive groups.
method Discriminative clustering using mutual information.
result Mutual information has been a cornerstone of discriminative clustering.
Deep learning models can discriminate against certain groups, requiring computational methods to ensure fairness.
problem Algorithmic discrimination in deep learning models affecting protected groups.
method Interpretability and mitigation approaches at different stages of deep learning lifecycle.
result Interpretability aids in diagnosing and mitigating algorithmic discrimination in deep learning.
Paper proposes a method to stabilize estimation of KL divergence using a discriminator in RKHS.
problem High variance and instability in estimating KL divergence using neural network discriminators.
method Developed a novel construction of the discriminator in RKHS, controlled its complexity, and proved the consistency of the estimator.
result Reduced variance and stabilized training of KL divergence estimates.
This paper analyzes implicit bias in Deep Linear Discriminant Analysis.
problem The implicit bias of Deep Linear Discriminant Analysis.
method Analyzing gradient flow on a L-layer diagonal linear network.
result Under balanced initialization, the network transforms additive updates into multiplicative updates, conserving the (2/L) quasi-norm.
We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying ran…
GNNs improve graph signal discrimination by adding nonlinearities.
problem Improving graph signal discrimination in physical networks.
method Analyzing the discriminability of GNNs and their relation to graph filter banks.
result GNNs are at least as discriminative as linear graph filter banks.
Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
TRAIL improves robot imitation learning by focusing on task-relevant features.
problem Discriminator networks learn spurious associations, providing poor reward signals.
method Constrained discriminator optimization to learn task-relevant rewards.
result TRAIL outperforms GAIL and behaviour cloning in robotic manipulation tasks.
Locally Valid and Discriminative prediction intervals for deep learning models.
problem Efficient and theoretically sound uncertainty quantification for deep learning models.
method Locally Valid and Discriminative prediction intervals (LVD) using kernel regression.
result Locally Valid and Discriminative prediction intervals (LVD) offer better performance and scalability compared to existing methods.
The paper addresses evaluating survival predictions using discrimination measures, finding a robust method to convert distributions to risks.
problem Evaluating survival distribution predictions with discrimination measures is challenging and often leads to unfair comparisons.
method The paper surveys existing methods and recommends summing over the predicted cumulative hazard as the most robust method to convert distributions to risks.
result Summing over the predicted cumulative hazard is the most robust method to convert distribution predictions to risk predictions.
Neural network based generative models with discriminative components are a powerful approach for semi-supervised learning. However, these techniques a) cannot account for model uncertainty in the estimation of the model's discriminative component and b) lack flexibility to capture complex stochastic patterns in the la…
Paper proposes faster incremental subclass discriminant analysis.
problem Efficiently classify subclasses in incremental data.
method Exact and approximate linear and kernelized solutions.
result Superior training time and accuracy compared to existing methods.
Semi-supervised learning uses unlabeled data to improve fairness without sacrificing accuracy.
problem Discrimination in machine learning models.
method Pseudo labeling, re-sampling, ensemble learning.
result Unlabeled data can help achieve a better balance between accuracy and fairness.
Develops DDC to improve clustering with deep neural networks.
problem Low-level indiscriminative representations and lack of pattern relationships in traditional clustering methods.
method Introduces global and local constraints to a deep neural network for adaptive relationship estimation and high-level representation learning.
result DDC outperforms current methods on multiple datasets.
The study tackles indirect discrimination in insurance pricing models.
problem Indirect discrimination in insurance pricing models.
method Presented a statistical model free of proxy discrimination.
result The canonical price in the model does not satisfy group fairness axioms.
Generative-discriminative method improves label prediction and instance generation.
problem Difficulty in obtaining high-quality labeled instances.
method Generative-discriminative complementary learning method using CC-GAN.
result Improves accuracy in predicting ordinary labels and generating high-quality instances.
IMKPL learns interpretable prototypes for better classification.
problem Efficient trade-offs between interpretability and prediction accuracy in kernel-based data.
method Local discrimination in feature space, condensed class-homogeneous neighborhoods, combined embedding.
result IMKPL achieves better interpretability and discriminative representation.